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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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supervision by AI and metabolomics experts at lifespin, as well as by participating professors from the TUM and HSWT. KEY RESPONSIBILITIES Simulating synthetic NMR spectra based on biophysical principles and
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from outside the University of Stuttgart can attend courses and seminars specially designed for simulation technology are able to exchange technical and methodical information are integrated
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time-scales: ab initio methods for the description of reaction processes, for the determination of electrochemical stabilities and for the optimisation of force fields; molecular dynamics simulations
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-field measurements, HPC-based numerical simulations, and laboratory experiments. Oldenburg is particularly renowned for its unique large wind tunnel with an active grid and leading expertise in developing
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Your Job: In this position, you will be an active part of our Simulation and Data Lab for Applied Machine Learning. Within national and European projects, you will drive the development of cutting
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biological physicists who ask how physical mechanisms shape functional biological patterns. We combine statistical physics, nonlinear dynamics, mathematical modeling and data-driven simulation with physics
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-driven simulation with physics-inspired data and image analysis, often in close collaboration with experimental partners, to identify physical principles behind biological dynamics and self-organization
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Your Job: The electrocatalytic interface engineering department led by Prof. Dr.-Ing. Simon Thiele focuses on manufacturing, analysis and simulation of functional materials to find an optimum
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to the different fields of research Annual proposal defences guarantee regular feedback and involvement of the candidate’s second supervisor in the research process Obligatory presentation of a competitive paper at